Twitter

Meta

EGMAYO, An Injury Impact Metric

Different injuries have different impacts. In this article I am going to look at how historical injuries have affected teams from the perspective of expected goals. Given each squad member’s xG per 90, and the number of games they missed, what’s the total amount of xG that was sidelined in a season?

I call this metric EGMAYO: Expected Goals Missed due to the Absence of Your Offence. Here are the top 10 EPL seasons by EGMAYO:

Season

Team

EGMAYO

2014

Arsenal

26.9

2010

Arsenal

23.3

2013

Arsenal

22.9

2012

Manchester City

19.4

2014

Liverpool

17.7

2013

Manchester City

17.4

2014

Manchester City

17.2

2014

Newcastle United

15.0

2011

Manchester United

14.8

2012

Manchester United

14.2

This indicates it’s not necessarily overly dramatic to point out that Arsenal’s injuries have had a big impact. Their lowest EGMAYO season was 2012, scoring 7.1, against an overall EPL average since 2010 of 6.7. Man City were title runners-up in their worst EGMAYO season:

Season

Team

Player

Games

Chance Quality per 90

Chance Quality missed

2012

Manchester City

Jack Rodwell

18

0.36

6.42

2012

Manchester City

Sergio Agüero

7

0.45

3.18

2012

Manchester City

Micah Richards

22

0.12

2.67

2012

Manchester City

Maicon

16

0.15

2.43

2012

Manchester City

Mario Balotelli

4

0.53

2.12

2012

Manchester City

David Silva

3

0.23

0.69

2012

Manchester City

Aleksandar Kolarov

6

0.10

0.57

2012

Manchester City

Vincent Kompany

7

0.06

0.42

2012

Manchester City

Samir Nasri

2

0.16

0.32

2012

Manchester City

Javi García

3

0.09

0.28

2012

Manchester City

James Milner

2

0.12

0.23

2012

Manchester City

Pablo Zabaleta

1

0.08

0.08

2012

Manchester City

Joleon Lescott

2

0.02

0.03

Obviously it’d be far more interesting if we could better capture Vincent Kompany’s 7 game absence from City’s back line, or David Silva’s expected assists missed in his 3 games, but we’re not there yet, which brings me to:

Caveats

Sometimes my kids go up to a box of toys and just empty it onto the floor, play briefly with a couple of things, and then bog off to let mummy and daddy deal with it. Perhaps I haven’t made this abundantly clear, but this is very much my approach to football stats. I enjoy cutting data up, throwing it haphazardly on the floor, and seeing what it looks like, especially to other people. I intend to return to this later to clean up, but I’d like to make a few things clear:

This metric takes no account of the squad members that come in and replace injured players. Obviously these replacements have their own output in terms of xG, which may even exceed the injured player. Ideally, we would capture all of this in a similar way to Chad Murphy’s model, or even in more detail to capture the strength of schedule faced during each injury.

It takes no account of the importance of midfielders, defenders or goalkeepers. It’s only interested in the xG per 90 of a injured players, and therefore is weighted heavily in favour of strikers. I’m merely using it as one way to look beyond raw injury stats, I’m not saying it’s the final destination.

The EGMAYO calculation uses the same season as the injury for xG per 90, so players injured early on, or starting the season injured, aren’t measured particularly accurately.